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Candidates must have received a Ph.D. by the commencement of employment. BNL policy requires that after obtaining their PhD, eligible candidates for research associate appointments may not exceed a combined
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and cannabinoids, combining molecular neuroscience, genetics, electroencephalography, imaging, and psychopharmacology. We are deeply committed to training the next generation of scientists and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
language processing, computer vision, medical image analysis, or a related area. Required Qualifications, Competencies, and Experience Demonstrated strong communication, teamwork, and leadership skills. Strong
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
looking for a postdoctoral fellow interested in developing either machine learning algorithms for high-resolution histopathology imaging/spatial-profiling data in combination with other modalities (e.g
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relying on optical measurement techniques for fluid mechanics, such as particle image or particle tracking velocimetry. Work with graduate and undergraduate students to fabricate and test prototypes, using
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required. Required Education and Experience Appropriate PhD in a related field Preferred Qualifications PhD. in Animal Science, Neurobiology, Biology, or related fields Experience in cell culture Experience
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candidate is expected to have access to a reliable internet connection for duties undertaken remotely. Qualifications Required Qualifications: A PhD degree in Biostatistics, Statistics, Computer Science or a
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. Required Qualifications: PhD or MD/PhD in a field(s) relevant to functional neuroimaging and neuroimaging data analysis. High-level experience with functional magnetic resonance imaging, spanning resting
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development, service delivery with an overarching objective connecting science with society. This encompasses the development of infrastructure and processes for data standardization, data warehousing, quality
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of existing ones for scientific applications; (ii) Large Language Models (LLMs) and multi-modal Foundation Models (iii) Large vision-language models (VLM) and computer vision techniques; and (iv) techniques